System and method for generating attenuation map

ABSTRACT

A method for generating attenuation map is disclosed. The method includes acquiring an anatomic image and PET data indicative of a subject, wherein the anatomic image comprises a plurality of voxels. The method also includes fetching a reference image to register the anatomic image, the reference image includes voxel segmentation information. The method further includes segmenting the anatomic image into a plurality of regions based on the voxel segmentation information. The method further includes generating a first attenuation map corresponding to the anatomic image by assigning attenuation coefficients to the plurality of regions. The method further includes calculating a registration accuracy between the anatomic image and the reference image. The method further includes determining a probability distribution of attenuation coefficient. The method further includes updating the first attenuation map iteratively based on the probability distribution of attenuation coefficient and the PET data to obtain a final attenuation map.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 15/317,376, filed on Dec. 8, 2016, which is a U.S.national stage under 35 U.S.C. § 371 of International Application No.PCT/CN2016/089433, filed on Jul. 8, 2016, designating the United Statesof America, the entire contents of each of which are hereby incorporatedby reference.

TECHNICAL FIELD

This application generally relates to the field of attenuationcorrection in image processing, and specifically, relates to a systemand method for attenuation map generation.

BACKGROUND

Emission computed tomography (ECT) includes, for example, a positronemission tomography (PET) system and a single photon emission computedtomography (SPECT) system. The PET system has been applied widely inimaging, especially for medical diagnosis and/or treatment of tumors,heart diseases, brain diseases, etc. A PET system may be integrated withone or more other imaging systems to form a multi-modality system.Exemplary multi-modality systems may include a positron emissiontomography-computed tomography (PET-CT) system, a positron emissiontomography-magnetic resonance (PET-MR) system, etc. In a PET scan,attenuation to various extents may occur when y-rays pass throughdifferent tissues of a subject because the attenuation degrees ofdifferent tissues to y-rays are different, causing distortion of a PETimage and/or PET data. To reconstruct a PET image and/or PET data, theattenuation may be corrected. An attenuation map may be generated in theprocess of attenuation correction.

In general, a method based on a CT image may be used to correct theattenuation of a PET image and/or PET data. However, the method based ona CT image (or referred to as a CT-based method) may cause a problem,such as low resolution of soft tissue imaging. A method based on amagnetic resonance (MR) image (or referred to as a MR-based method) maybe used to correct the attenuation of a PET image and/or PET data.Compared to a CT-based method, the MR-based method may be provide a highsensitivity and/or high accuracy. However, the MR-based method may haveone or more shortcomings in view of at least the followings. There is nomapping relation between the attenuation coefficient distribution of anMR image and the attenuation coefficient distribution of a correspondingPET image. Moreover, there may exist an edge truncation artifact in MRimaging.

Thus, there exists a need in the field to provide a method and systemfor attenuation correction that may address these and other technicalchallenges.

SUMMARY

Some embodiments of the present disclosure relate to a method and systemfor generating an attenuation map. The method may include one or more ofthe following operations. An anatomic image and PET data indicative of asubject may be acquired. A reference image may be fetched from adatabase. The reference image may be registered to the anatomic imageand include voxel segmentation information. The anatomic image may besegmented into a plurality of regions based on the voxel segmentationinformation of the reference image. A first attenuation mapcorresponding to the anatomic image may be generated by assigningattenuation coefficients to the plurality of regions. A registrationaccuracy between the anatomic image and the reference image may becalculated. A probability distribution of the attenuation coefficient ofthe voxel of the anatomic image may be determined based on theregistration accuracy. The first attenuation map may be updatediteratively based on the probability distribution and the PET data toobtain a final attenuation map.

In some embodiments, the reference images may be fetched based on one ormore types of characteristic information of the subject. Thecharacteristic information may include height, weight, gender of thesubject, an area of the subject to imaging, or the like, or acombination thereof.

In some embodiments, the determination of the probability distributionof attenuation coefficient may include one or more of the followingoperations. A statistical probability indicative of a voxel belonging toat least one region of the plurality of regions based on theregistration accuracy may be calculated. The probability distribution ofattenuation coefficient of the voxel may be acquired based on thestatistical probability. If the probability distribution of attenuationcoefficient is less than 1, the voxel may be designated belonging to atleast two regions of the plurality of regions. If the probabilitydistribution of attenuation coefficient of the voxel is 1, the voxel maybe designated belonging to a certain region of the plurality of regions,wherein the certain region of the plurality of regions may comprise aplurality of voxels with a same probability distribution of attenuationcoefficient, and the plurality of voxels of the certain region areassigned with a same attenuation coefficient.

In some embodiments, the anatomic image may be a MR image, a CT image,or the like, or any combination thereof.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting examples,in which like reference numerals represent similar structures throughoutthe several views of the drawings, and wherein:

FIG. 1 illustrates an exemplary block diagram of an imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is an exemplary flowchart illustrating a process for generatingan attenuation map according to some embodiments of the presentdisclosure;

FIG. 3A is an exemplary attenuation map of transverse section of theregion between the lungs and the abdomen of a subject generated by a PETscan according to some embodiments of the present disclosure;

FIG. 3B is an exemplary first attenuation map of transverse section ofthe region between the lungs and the abdomen of the same subject as inFIG. 3A according to some embodiments of the present disclosure;

FIG. 3C is an exemplary attenuation map that generated by segmenting ananatomic image into a plurality of regions based on voxel segmentationinformation of a reference image according to some embodiments of thepresent disclosure; and

FIG. 3D is an exemplary final attenuation map of transverse section ofthe region between the lungs and the abdomen of the same subject as inFIG. 3A and FIG. 3B according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

It will be understood that the term “system,” “unit,” “module,” and/or“block” used herein are one method to distinguish different components,elements, parts, section or assembly of different level in ascendingorder. However, the terms may be displaced by other expression if theymay achieve the same purpose.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

The terminology used herein is for the purposes of describing particularexamples and embodiments only, and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include,”and/or “comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof.

Provided herein are systems and components for non-invasive imaging,such as for disease diagnostic or research purposes. The imaging systemmay find its applications in different fields such as, for example,medicine or industry. The radiation used herein may include a particleray, a photon ray, or the like, or any combination thereof. The particleray may include neutron, proton, electron, μ-meson, heavy ion, or thelike, or any combination thereof. The photon beam may include X-ray,γ-ray, α-ray, β-ray, ultraviolet, laser, or the like, or any combinationthereof.

Merely by way of example, the imaging system may be a positron emissiontomography (PET) system, an emission computed tomography (ECT) system, amulti-modality system, or the like, or any combination thereof.Exemplary imaging systems may include a PET system, a multi-modalitysystem, or the like, or any combination thereof. Exemplarymulti-modality systems may include a computed tomography-positronemission tomography (CT-PET) system, a magnetic resonance-positronemission tomography (MR-PET) system, etc.

In a multi-modality system, the mechanisms through which differentimaging modalities operate or function may be the same or different.Accordingly, the imaging information may also be the same or different.For example, in some embodiments, the imaging information may beinternal and/or external information, and may be functional and/orstructural information of a subject. The internal information may bestored in and/or generated by the multi-modality system. The externalinformation may be stored in and/or generated by one or more otherimaging systems. In some embodiments, the imaging information ofdifferent modalities may complement one another, thereby providing a setof imaging data describing the subject from different analytical angles.For example, in some embodiments, the multi-modality imaging may achievethe merging of morphological and functional images.

In some embodiments, the multi-modality system may include a computedtomography (CT) imaging modality, which is an imaging method thatcombines multiple X-ray projections taken from different angles toproduce detailed cross-sectional images of an internal area of asubject. Thus, CT imaging information may offer medical practitionersprecise, three-dimensional views of certain internal parts of thesubject, such as soft tissues, bones, blood vessels, organs of a humanbody, without performing invasive procedures on the subject. In someembodiments, the multi-modality system may include an ultrasound imagingmodality, which is an imaging technology that uses high frequency soundwaves to create images of the internal of the subject. In someembodiments, the ultrasound imaging modality may send sound waves intothe body and convert the returning sound echoes into an image.

In some embodiments of the present disclosure, the multi-modalityimaging system may include modules and/or components for performingpositron emission tomography (PET) imaging and analysis. The term“positron emission tomography or PET” as used herein refers to anon-invasive radiology procedure applicable to a subject that generatesimage information reflecting or corresponding to functional processestaking place in the internal body.

During a PET scan or study, PET tracer molecules are first introducedinto the subject before an imaging session begins. The term “PET tracer”or “tracer” as used herein refers to a substance that may undergocertain changes under the influence of an activity or functionalitywithin the subject, whose activity and/or functionality are to bevisualized and/or studied by the PET. Such changes may be chemicaland/or physical, during which the PET tracers may emit positrons, namelythe antiparticles of electrons. A positron has the same mass and theopposite electrical charge as an electron, and it undergoes anannihilation with an electron (that may naturally exist in abundancewithin the subject) as the two particles collide. An electron-positronannihilation may result in two 511 keV gamma photons, which upon theirown generation, begin to travel in opposite directions with respect toone another. The PET imaging modules of the present system may obtainthe trajectory and/or dose information of the gamma photons to determinethe location and concentration of the PET tracer molecules within thesubject.

Many basic elements that make up organic matters have positron-emittingisotopes, including carbon (¹¹C), nitrogen (¹³N), oxygen (¹⁵O), andfluorine (¹⁸F). Accordingly, in some embodiments, the PET tracermolecules of the present disclosure are organic compounds containing oneor more of such positron-emitting isotopes. These type of PET tracermolecules are either similar to naturally occurring substances orotherwise capable of interacting with the functionality or activity ofinterest within the subject. Hence, distributional information of thePET tracer may be reliably used as an indicator of the subjectfunctionality.

Merely by way of example, the PET tracer molecule is¹⁸F-fluoro-deoxy-glucose (¹⁸F-FDG), a radioactive analogue of glucose.¹⁸F-FDG follows a similar metabolic pathway to glucose in vivo, butremains trapped within tissues. Thus, in vivo distribution of ¹⁸F-FDGmapped by the present PET imaging may indicate glucose metabolicactivity, which may be of interest in oncology as proliferating cancercell have higher than average rate of glucose metabolism. Merely by wayof example, the PET tracer molecule is ¹³N—NH₃ for functional imaging ofmyocardial perfusion. Particularly, in these embodiments, in vivodistribution of ¹³N—NH₃ may be used to distinguish between viable andnon-viable tissue in poorly perfused areas of the heart, which may be ofinterest in cardiology to identify candidates for coronary by-passsurgery.

Further provided below is a non-exhaustive list of exemplary organic PETtracers that may be used in connection with the present system. In someembodiments, the PET tracer molecule is ¹¹C-methionine, where it acts asa marker for protein synthesis in oncology. In some embodiments, the PETtracer molecule is ¹¹C-flumazenil, where it acts as a marker forbenzodiazepine receptor activity in epilepsy. In some embodiments, thePET tracer molecule is ¹¹C-raclopride, where it acts as a marker for D2receptor agonist activity for diagnosis of movement disorders. In someembodiments, the PET tracer molecule is ¹⁵O-carbon dioxide or ¹⁵O-water,where it acts as a marker for blood perfusion in brains. In someembodiments, the PET tracer is ¹⁸F-fluoride ion, where it acts as amarker for bone metabolism in oncology; in some embodiments, the PETtracer molecule is 18F fluoro-mizonidazole, where it acts as a markerfor hypoxia in assessing patient response to radiotherapy in oncology.In some embodiments, multiple different PET tracers may be used incombination to produce complementing sets of functional data.

The above types of imaging modalities that may be included in thepresent system are not exhaustive and are not limiting. After consultingthe present disclosure, one skilled in the art may envisage numerousother changes, substitutions, variations, alterations, and modificationswithout inventive activity, and it is intended that the presentdisclosure encompasses all such changes, substitutions, variations,alterations, and modifications as falling within its scope.

This is understood that the following descriptions are provided inconnection with image processing for illustration purposes and notintended to limit the scope of the present disclosure. The imageprocessing disclosed herein may be used for purposes other than medicaltreatment or diagnosis. For instance, the image processing may be usedfor purposes of detecting a fracture within a structure or itsprogression over time, a non-uniform portion within a piece of material,etc.

For illustration purposes, the following description is provided to helpbetter understanding an image processing. It is understood that this isnot intended to limit the scope of some embodiments of the presentdisclosure. For persons having ordinary skills in the art, a certainamount of variations, changes and/or modifications may be deducted underguidance of some embodiments of the present disclosure. However, thosevariations, changes and/or modifications do not depart from the scope ofsome embodiments of the present disclosure.

Some embodiments of the present disclosure relate to image processing.Specifically, some embodiments of the present disclosure relate to amethod and system for attenuation map generation and imagereconstruction. The process of attenuation map generation and imagereconstruction as illustrated in some embodiments of the presentdisclosure may be automated, or semi-automated. It may be implemented ina computer-aided and automated medical diagnosis and/or treatmentsystem.

FIG. 1 illustrates an exemplary block diagram of an imaging system 100according to some embodiments of the present disclosure. It should benoted that the imaging system 100 described below is merely provided forillustration purposes, and not intended to limit the scope of thepresent disclosure. Merely by way of example, the imaging system may bea positron emission tomography (PET) system, a computed tomography (CT)system, a magnetic resonance (MR) system, a multi-modality system, orthe like, or any combination thereof. Exemplary multi-modality systemmay include a computed tomography-positron emission tomography (CT-PET)system, a magnetic resonance-positron emission tomography (MR-PET)system, etc. A subject may be positioned in the PET system, and PET dataand/or PET image of the subject may be acquired from the PET system. PETimage may be acquired from the reconstruction of PET data.

As shown in FIG. 1, the imaging system 100 may include a controller 110,a gantry 120, a signal processor 130, a coincidence counter 140, astorage 150, a reconstruction unit 160, a display 170, and an operationunit 180. The controller 110 may be configured to control the processingof imaging, the processing of attenuation map generation, the processingof image reconstruction, or the like, or any combination thereof.

The controller 110, the gantry 120, the signal processor 130, thecoincidence counter 140, the storage 150, the reconstruction unit 160,the display 170, and the operation unit 180 may be connected with eachother. The connection may be wired or wireless. The wired connection mayinclude using a metal cable, an optical cable, a hybrid cable, aninterface, or the like, or any combination thereof. The wirelessconnection may include using a Local Area Network (LAN), a Wide AreaNetwork (WAN), a Bluetooth, a ZigBee, a Near Field Communication (NFC),a Wi-Fi, a Wireless a Wide Area Network (WWAN), or the like, or anycombination thereof. One or more of the controller 110, the signalprocessor 130, the coincidence counter 140, the storage 150, thereconstruction unit 160, the display 170 and the operation unit 180 maybe integrated in a computer, a laptop, a cell phone, a mobile phone, aportable equipment, a pad, a Central Processing Unit (CPU), anApplication-Specific Integrated Circuit (ASIC), an Application-SpecificInstruction-Set Processor (ASIP), a Graphics Processing Unit (GPU), aPhysics Processing Unit (PPU), a Digital Signal Processor (DSP), a FieldProgrammable Gate Array (FPGA), a Programmable Logic Device (PLD), aController, a Microcontroller unit, a Processor, a Microprocessor, anARM, or the like, or any combination thereof.

The gantry 120 may be configured to generate an electric signal relatingto a subject. The term “subject” as used herein may refer to any organicor inorganic mass, natural or man-made, that has a chemical,biochemical, biological, physiological, biophysical and/or physicalactivity or function. Exemplary embodiments of a subject pertaining tothe present disclosure may include cells, tissues, organs or wholebodies of human or animal. Other exemplary embodiments include but notlimited to man-made composition of organic and/or inorganic matters thatare with or without life. In some embodiments, the subject may be ahuman patient. The gantry may include a plurality of detector rings. Adetector ring may include a plurality of circumferentially arrangeddetectors. The gantry may detect radiation emitted from a subject towhich a radioisotope may be administered by detectors in the detectorring. The gantry may include a field of view (FOV), a subject may belocated in the FOV. The subject P may be placed on a patient couch. Insome embodiments, a type of PET tracer molecules may be introduced orinjected into the subject P for PET imaging. The detector may detectannihilation gamma-rays emitted from inside the subject P to generate anelectric signal, which may be in accordance with the quantity of lightof the detected pair annihilation gamma-rays.

The signal processor 130 may be configured to generate single event databased on the electric signal generated by detectors of the gantry 102.For purposes of illustration, the signal processor may perform detectiontime measurement processing, position calculation processing, energycalculation processing, or the like, or any combination thereof. Forexample, in the detection time measurement processing, the signalprocessor 130 may measure the detection time of gamma-rays by thedetector. More specifically, the signal processor 130 may monitor thepeak value of the electric signal generated from the gantry 120. Thesignal processor 130 may register the time when the peak value of anelectric signal exceeds a threshold as a detection time. The signalprocessor 130 may detect an annihilation gamma-ray by detecting when theamplitude of the electric signal exceeds the threshold. In the positioncalculation, the signal processor 130 may calculate an incident positionof annihilation gamma-rays based on the electric signal. In the energycalculation, the signal processor 130 may determine an energy value ofan annihilation gamma-ray incident based on the electric signal. Thesingle event data may be energy values, position coordinates, detectiontimes regarding single events, or the like, or a combination thereof.Single event data may be generated when annihilation gamma-rays aredetected. The term “single event” as used herein may refer to thedetection of a gamma photon by a detector of the gantry 120.

The coincidence counter 140 may be configured to process single eventdata relating to a plurality of single events. For the purposes ofillustration, the coincidence counter 140 may determine event data oftwo single events that fall within a preset time interval amongcontinuously supplied single event data. The time interval may be setto, for example, approximately 6 nanoseconds to 18 nanoseconds. A pairof single events detected in the time interval may be deemed tooriginate from a pair of gamma-rays generated from the same annihilationevent. A pair of single events resulting from an annihilation event maybe called a coincidence event. The line connecting a pair of thedetectors that may detect the coincidence event may be called line ofresponse (LOR). The coincidence counter 140 may count coincidence eventsfor each LOR. Data relating to coincidence events may be referred to ascoincidence event data.

The storage 150 may be configured to store data relating to thegeneration of an attenuation map and/or image processing. The datastored in the storage 150 may include coincidence event data, singleevent data, a database, imaging data, an attenuation coefficient, anattenuation map, or the like, or any combination thereof. The imagingdata may include an MR image, a SPECT image, a PET image, a CT image, orthe like, or any combination thereof.

In some embodiments, the database may include one or more anatomicimages, voxel segmentation information, or the like, or any combinationthereof. In some embodiments, the database may include a plurality ofdictionary elements (D₁, D₂, D₃, . . . D_(X), . . . , D_(Y-1), D_(Y)),in which Y may denote the total number of dictionary elements. Adictionary element DX (1≤X≤Y) in the database may include tworegistering images, one of which may be an emission image (which may bedenoted as IMGX), one of which may be voxel segmentation information(which may be denoted as

_(X), 1≤X≤Y), or the like, or any combination thereof. In someembodiments, the emission image may include an anatomic image and voxelsegmentation information. The anatomic image may include atwo-dimensional (2D) image and a three-dimensional (3D) image. Theanatomic image may include an emission image, a tomographic image, orthe like, or any combination thereof. The anatomic image may include anMR image, a SPECT image, a PET image, a CT image, or the like, or anycombination thereof. The emission image may include an MR image, a PETimage, a CT image, or the like, or any combination thereof. Thecharacteristic information of the subject indicated by a reference imagemay include height, weight, gender, age, medical conditions of thesubject, medical history of the subject, birthplace of the subject, anarea of the subject to imaging, or the like, or any combination thereof.Descriptions regarding a reference image may be found elsewhere in thepresent disclosure.

In some embodiments, the data stored in the storage 150 may be acquiredfrom one or more of the controller 110, the gantry 120, the signalprocessor 130, the coincidence counter 140, the reconstruction unit 160,the display 170 and the operation unit 180. Merely by way of example,the data may include the imaging data acquired from the gantry 120. Thedata may include information and/or instructions acquired from theoperation unit 180. The information and/or instructions may include acharacteristic information of the subject, a way of displaying, a way ofstoring, or the like, or any combination thereof. The characteristicinformation of the subject may include height, weight, gender, age,medical conditions of the subject, medical history of the subject,birthplace of the subject, an area of the subject to imaging, or thelike, or any combination thereof. The information of the area of thesubject that is imaged may further include the subject position, such asthe subject lying pronely or supinely on the couch when the subject isimaged, information of an organ, information of a tissue, or the like,or any combination thereof.

The data stored in the storage 150 may be acquired from or output to anexternal storage device including, for example, a floppy disk, a harddisk, a CD-ROM, a network server, a cloud server, a wireless terminal,or the like, or any combination thereof. The storage 150 may store databy way of electric energy, magnetic energy, optical energy, or a virtualstorage resource, etc. The storage 150 may store data by way of electricenergy may include Random Access Memory (RAM), Read Only Memory (ROM),flash memory, or the like, or any combination thereof. The storage 150may store data by way of magnetic energy may include a hard disk, afloppy disk, a magnetic tape, a magnetic core memory, a bubble memory, aUSB flash drive, or the like, or any combination thereof. The storage150 may store data by way of optical energy may include CD (CompactDisk), VCD (Video Compact Disk), or the like, or any combinationthereof. The storage 150 may store data by way of virtual storageresources may include cloud storage, a virtual private network, and/orother virtual storage resources. The method to store data may includesequential storage, link storage, hash storage, index storage, or thelike, or any combination thereof.

The reconstruction unit 160 may be configured to reconstruct imagingdata. The imaging data may represent the spatial distribution ofconcentration of a radioisotope inside a subject based on coincidenceevent data of a plurality of coincidence events. In some embodiment, theimaging data may be PET data of the subject P based on coincidence eventdata. An emission image may be generated in the reconstruction unit 160by reconstructing the PET data.

In some embodiment, the reconstruction of the imaging data may be basedon time-of-flight (TOF) determination. A detection time difference of apair of annihilation gamma-rays may be measured and/or recorded by thetechnique of TOF. The probability of presence of the pair annihilationpoint in each voxel on the LOR may be different depending on thedetection time difference of coincidence events. In some embodiments, anattenuation map may be generated in reconstruction unit 160 toreconstruct the imaging data.

The display 170 may be configured to display one or more images in adisplay device. The display may include a CRT display, a liquid crystaldisplay, an organic EL display, or plasma display, or the like, or anycombination thereof. The display device may include a computer, alaptop, a cell phone, a mobile phone, a portable equipment, a pad, aglass, a projector, a virtual reality device, or the like, or anycombination thereof.

The operation unit 180 may be configured to receive one or moreinformation and/or instructions by an operator via an input device. Theinput device may include a keyboard, a mouse, a button, or a touch keypanel, a touch screen, or the like, or any combination thereof. Theinformation and/or instructions may include a characteristic informationof the subject, a way of displaying, a way of storing, or the like, orany combination thereof. The characteristic information of the subjectmay include height, weight, gender, age, medical conditions of thesubject, medical history of the subject, birthplace of the subject, anarea of the subject to imaging, or the like, or any combination thereof.The information of the area of the subject to imaging may furtherinclude the subject position, such as the subject lying pronely orsupinely on the couch when the subject is imaged, information of anorgan, information of a tissue, or the like, or any combination thereof.

It should be noted that the imaging system described above is merelyprovided for the purposes of illustration, and not intended to limit thescope of some embodiments of the present disclosure. Apparently forpersons having ordinary skills in the art, numerous variations andmodifications may be conducted under the teaching of some embodiments ofthe present disclosure without inventive activity. Some embodiments ofthe present disclosure is intended to encompass all those variations andmodifications as falling under its scope. In some embodiments, thefunctioning of the storage 150 may be realized in the reconstructionunit 160. Merely by way of example, the database may be stored in thereconstruction unit 160. The information and/or instructions acquiredfrom the operation unit 180 may be stored in the reconstruction unit160.

FIG. 2 is an exemplary flowchart illustrating a process for generatingan attenuation map according to some embodiments of the presentdisclosure. In step 201, an anatomic image and PET data indicative of asubject may be acquired, the anatomic image may include a plurality ofvoxels. The anatomic image may be acquired from the imaging system 100,or acquired from an external memory. The external memory may include ahard disk, a USB flash drive, a cloud storage, a server, or the like, orany combination thereof. The anatomic image may include an MR image, aCT image, or the like, or any combination thereof. The anatomic imagemay be acquired at a time before step 202 is performed. For example, theanatomic image may be acquired real-timely or not real-timely. Merely byway of example, in the MR-PET system, an MR image may be acquired afterthe injection of a radioisotope into the subject, and before PETscanning. In some embodiments, the anatomic image may be fetched fromthe storage 150.

In some embodiments, the MR image(s) may be acquired from a magneticresonance (MR) system, a magnetic resonance-positron emission tomography(MR-PET) system, a magnetic resonance-single photon emission computedtomography (MR-SPECT) system, or the like, or any combination thereof.In some embodiments, the CT image(s) may be acquired from a computedtomography (CT) system, a computed tomography-positron emissiontomography (CT-PET) system, or the like, or any combination thereof.

In step 202, a reference image may be fetched from a database, thereference image may be configured to register the anatomic image. Thedatabase may include a plurality of dictionary elements as described inconnection with FIG. 1. A dictionary element may include an anatomicimage, voxel segmentation information, characteristic informationrelating to a subject, or the like, or any combination thereof. Thecharacteristic information relating to the subject indicated by thereference image may include height, weight, gender, age, medicalconditions of the subject, medical history of the subject, birthplace ofthe subject, an area of the subject to imaging, or the like, or anycombination thereof.

The reference image may be fetched from the database to match theanatomic image. For example, if the characteristic information indicatedby the anatomic image matches the characteristic information indicatedby the anatomic image of a dictionary element, the anatomic image may bedetermined as a reference image.

During a diagnosis of a patient, due to the breathing of the patient,peristalsis of the patient's viscera, or an alteration of the patient'sposition, images obtained for the patient at different times maydistort. Images of some viscera for different patients may differ aswell. An anatomic image of a dictionary element and an anatomic imagemay be registered according to various registration methods including,for example, an optical flow method, a registration method based on oneor more feature points, a registration method based on a contour, aregistration method based on grey scale information, etc.

In some embodiments, an exemplary optical flow process may include oneor more of the following operations. An initial deformation field may bespecified in the anatomic image and a reference image, respectively. Thereference image may be one of image in a dictionary element. The term“deformation field” as used herein may refer to a set of vectorsdescribing how to warp one image to match another. The initialdeformation field may be used for transforming and comparing theanatomic image and the reference image. An updated deformation field ofthe anatomic image may be calculated based on the optical flow method.An anatomic image (of a dictionary element) may be selected to be usedas the reference image based on, for example, a degree of matching withrespect to the anatomic image. In some embodiments, the anatomic image(of a dictionary element) that has a highest degree of matching, amongthe dictionary elements, with respect to the anatomic image may beselected and be designated as the reference image. A deformation fieldof the selected anatomic image may be calculated based on the opticalflow method. The initial deformation field may be updated to generate anupdated deformation field. The update may be performed based on, forexample, the gradient of the optical flow cost function, etc. Thegradient of the optical flow cost function may then be calculated basedon the updated anatomic image transformed using the deformation field.The updating of the deformation field and the calculation of thegradient of the optical flow cost function may be iteratively performeduntil a condition, e.g., convergence, is met.

In some embodiments, a registration method based on feature points maybe employed in the present disclosure. For instance, attachment markersmay be detected in an anatomic image and a selected anatomic image (of adictionary element) that has the highest matching degree with theanatomic image, a registration method based on external feature pointsmay be employed. In some embodiments, anchor points or extreme pointsmay be detected in a subject, a registration method based on internalfeature points may be employed.

In some embodiments, a registration method based on a contour may beemployed. One or more curves and/or the contour of an anatomic image maybe extracted; the anatomic image (of a dictionary element) that has adegree of matching with the anatomic image may be selected. Forinstance, the anatomic image (of a dictionary element) that has ahighest degree of matching, among the dictionary elements, with respectto the anatomic image may be selected. One or more corresponding curvesand/or the contour of the selected emission image, relative to theanatomic image, may be extracted. Geometric transformation may bedetermined based on the extracted curves and/or contour.

In some embodiments, a registration method based on grey scaleinformation of one or more pixels may be employed in the presentdisclosure. For instance, statistic information of images, mutualinformation, grey space entropy, etc., may be employed to obtain areference image of an anatomic image.

A dictionary element of the database may include an anatomic image andvoxel segmentation information corresponding to the anatomic image.According to the reference image determined in step 202, the voxelsegmentation information corresponding to the reference image may beacquired. The registration methods described above and otherregistration methods may be used to register the anatomic image and thereference image. For example, the registration method may be based onAtlas registration.

In step 203, the anatomic image may be segmented into a plurality ofregions based on the voxel segmentation information of the referenceimage. In step 204, a first attenuation map corresponding to theanatomic image may be generated by assigning attenuation coefficients tothe plurality of regions (e.g., a spatial region, which may be alsoreferred to as a geometrical region, etc.). The attenuation coefficientsmay be stored in the database, and the attenuation coefficients maycorrespond to one or more spatial regions. In some embodiments, theattenuation coefficients may be acquired from a user and/or input by theuser. A spatial region may be a bone, a tissue, an organ, a vessel,viscera, or the like, or any combination thereof. Merely by way ofexample, the voxel segmentation information of a dictionary element thathas the highest degree of matching with respect to the patient'sphysiological information may be distorted to provide voxel segmentationinformation of the anatomic image. For example, a distortion field maybe obtained to register an anatomic image and an anatomic image of thedictionary, and the distortion field may be applied to the voxelsegmentation information of the anatomic image to generate voxelsegmentation information of the anatomic image. The voxel segmentationinformation of the anatomic image may be used to segment the anatomicimage into a plurality of regions. The voxel segmentation of an anatomicimage may be based on segmentation, and the voxels of the anatomic imagemay be segmented into a plurality of regions (S₁, S₂, S₃, . . . ,S_(Q-1), S_(Q)) based on segmentation and registration, in which Q (Q>1)may denote the number of regions. The plurality of regions may includeat least some of the voxels of the anatomic image.

On the basis of the plurality of regions and the topological informationof the voxels of the anatomic image, the voxel segmentation informationof a dictionary element may include corresponding attenuationcoefficients. The attenuation coefficients corresponding to voxels ofthe plurality of regions may be determined based on the attenuationcoefficients of the dictionary element. For example, some voxels of theanatomic image may be segmented and designated as a bone region, and theattenuation coefficient of a bone of the dictionary element may beassigned to the bone region of the anatomic image. As another example,some voxels of an anatomic image may be segmented and designated as alung tissue, and the attenuation coefficient of lung tissue of thedictionary element may be assigned to the lung tissue of the anatomicimage.

FIG. 3A illustrates an attenuation map of a transverse section of theregion between the lungs and the abdomen of a subject generated by a PETscan. Due to the variations of absorption of photons by differentportions of a human body, the attenuation effect by different portionsof the human body may be different (e.g., different grey valuecorresponding to different attenuation coefficients). For example, theportion with a higher grey value may correspond to right lobe of liver,and the portion with a lower grey value may correspond to stomach. Thewhite portion may correspond to a portion of lung, and the black portionmay correspond to a portion of bone.

FIG. 3B illustrates an initial attenuation map of a transverse sectionof the region between the lungs and the abdomen of the same subject asin FIG. 3A. Two levels of grey value may be seen in FIG. 3B, suggestingthat 2 attenuation coefficients may be associated with the image. Theattenuation coefficient of the liver, the stomach, and the spleen may besimilar or essentially the same. As used herein, “essentially,” as in“essentially the same,” “essentially approximate,” etc., with respect toa parameter or a characteristic may indicate that the variation iswithin 2%, or 5%, or 8%, or 10%, or 15%, or 20% of the parameter or thecharacteristic. As shown in FIG. 3B, it may be difficult to recognizethe boundary regions between organs and/or tissues.

FIG. 3C illustrates an attenuation map that generated based onsegmenting an anatomic image into a plurality of regions based on thevoxel segmentation information of a reference image. As shown in FIG.3C, different portions may have different grey values (differentattenuation coefficients). The shape of each organ may be difficult tobe identified. The boundary regions of lung and liver may include voxelswhose belongings are not determined. The boundary regions of lung andbone may include voxels whose belongings are not determined as well.Meanwhile the boundary of the organs is not clear. The attenuation mapthat is generated based on segmenting an anatomic image into a pluralityof regions based on the voxel segmentation information of a referenceimage may essentially approximate that shown in FIG. 3C. Due to therestriction caused by the accuracy of a registration method, it may bedifficult to determining which regions a voxel at a boundary regionbelong to. Therefore, a probability distribution of attenuationcoefficients of voxels may be employed in the present disclosure torefine the plurality of regions as described above, and determine whichregions the voxels of boundary regions belong to.

In step 205, the registration accuracy between the anatomic image andthe reference image may be calculated. The registration accuracy may becalculated based on mutual information (MI) between the anatomic imageand the reference image, variance between the anatomic image and thereference image, or the like, or any combination thereof.

In some embodiments, the registration accuracy may be determined with aformula of:

$\begin{matrix}{{{R\left( {{A\left( {x,y,z} \right)},{M\left( {x,y,z} \right)}} \right)} = {{\left( {{A\left( {x,y,z} \right)} - \frac{\int_{\upsilon \in S}{{A\left( {x,y,z} \right)}\ d\;\upsilon}}{\int_{\upsilon \in S}{d\;\upsilon}}} \right) \times \left( {{M\left( {x,y,z} \right)} - \frac{\int_{\upsilon \in S}{{M\left( {x,y,z} \right)}\ d\;\upsilon}}{\int_{\upsilon \in S}{d\;\upsilon}}} \right)}}},} & \left( {{Formula}\mspace{14mu} 1} \right)\end{matrix}$in which A may denote the reference image, M may denote the anatomicimage indicative of the subject, R (A, M) may denote the registrationaccuracy between the reference image A and the anatomic image M; S maydenote the plurality of regions (S₁-S_(M)) generated in step 203; v maydenote all the voxels of a certain region of the regions from S₁ toS_(Q), wherein the certain region comprise a plurality of voxels. ∥ maydenote an absolute value function; (x, y, z) may denotethree-dimensional coordinate of a voxel in the plurality of regions, xmay denotes x-axis coordinate, y may denote y-axis coordinate, and z maydenote z-axis coordinate.

For the purposes of illustration, the registration accuracy may show adegree of similarity between the reference image A and the anatomicimage M. The reference image A and the anatomic image M may be of a sametype. For instance, both the reference image A and the anatomic image Mmay be MR images, CT images etc. The reference image A and the anatomicimage M may be of different types. For instance, the reference image Aand the anatomic image M may be an MR image and a CT image,respectively.

In step 206, the probability distribution of attenuation coefficients ofvoxels of the anatomic image may be determined based on the registrationaccuracy. The term “probability distribution of attenuation coefficient”may refer to the probability distribution of different attenuationcoefficients of a voxel. In some embodiments, the probabilitydistribution of various attenuation coefficients of a voxel {right arrowover (r)} may be determined, in which the probability P(μ_(j)) for anattenuation coefficient μ_(j) may be determined with a formula of:P(μ_(j))=Σ_(w) P({right arrow over (r)}∈S _(w))G(μ_(j) −U_(w)).  (Formula 2)

In Formula 2, μ_(j) may denote an attenuation coefficient of a voxel{right arrow over (r)}; P(μ_(j)) may denote a probability of theattenuation coefficient μ_(j) of the voxel {right arrow over (r)};P({right arrow over (r)}∈S_(w)) may denote a statistical probability ofthe voxel belonging to a certain region S_(w); U_(w) may denote theattenuation coefficient of region S_(w); and G may denote a Gaussiandistribution function (often simply referred to as a Gaussian or aGaussian function). It should be noted that the certain region maycomprise a plurality of voxels with a same probability distribution ofattenuation coefficient, and the plurality of voxels of the certainregion may be assigned with a same attenuation coefficient.

In some embodiments, the probability distribution of attenuationcoefficient of a voxel may be determined with a formula of:P(μ_(j))=Σ_(w) f ₁(P({right arrow over (r)}∈S _(w)))G(μ_(j) −U _(w) ,f₂(P({right arrow over (r)}∈S _(w)))),  (Formula 3)in which μ_(j) may denote an attenuation coefficient of a voxel {rightarrow over (r)}; P(μ_(j)) may denote a probability distribution ofattenuation coefficient of the voxel {right arrow over (r)}; P({rightarrow over (r)}∈S_(w)) may denote a statistical probability of the voxelbelonging to a certain region S_(w); the certain region S_(w) may be aspatial region; U_(w) may denote an attenuation coefficientcorresponding to the region S_(w) which the voxel {right arrow over (r)}belongs to; f₁(x) may denote an increasing function, in which x ∈[0, 1];and G(μ_(j)−U_(w), f₂(P({right arrow over (r)}∈S_(w)))) may denote aGaussian function, with a variance f₂(P({right arrow over (r)}∈S_(w))),in which f₂(x) may denote a decreasing function, wherein x∈[0, 1].

The statistical probability of a voxel belonging to a certain region(often simply referred to as P({right arrow over (r)}∈S_(w))) may beused to calculate P(μ_(j)). P({right arrow over (r)}∈S_(w)) may bedetermined with a formula of:

$\begin{matrix}{{{P\left( {\overset{\rightarrow}{r} \in S_{w}} \right)} = \frac{\int_{v \in {V{(\overset{\rightarrow}{r})}}}{{R\left( {A,M} \right)}{dv}}}{\sqrt{\int_{v \in {V{(\overset{\rightarrow}{r})}}}{{R\left( {A,A} \right)}{dv}*{\int_{v \in {V{(\overset{\rightarrow}{r})}}}{{R\left( {M,M} \right)}{dv}}}}}}},} & \left( {{Formula}\mspace{14mu} 4} \right)\end{matrix}$in which {right arrow over (r)} may denote a voxel of the plurality ofregions of the anatomic image, V({right arrow over (r)}) may denote athree-dimensional (3D) region (such as a spatial region), and the voxel{right arrow over (r)} may be the center of V({right arrow over (r)});S_(w) may denote a region of the plurality of regions of the referenceimage A. P({right arrow over (r)}∈S_(w)) may denote the statisticalprobability of the voxel {right arrow over (r)} belonging to a certainregion Sw (1≤w≤Q).

When the reference image A and the anatomic image M in the region ofV({right arrow over (r)}) are totally same or proportionally same,P({right arrow over (r)}∈S_(w))=1, which may indicate that the voxel{right arrow over (r)} belongs to the region S_(w), and further indicatethat voxel segmentation information of the anatomic image M may be sameto voxel segmentation information of the reference image. When thereference image A and the anatomic image M in the region of V({rightarrow over (r)}) are not same or not proportionally same, 0<P({rightarrow over (r)}∈S_(w))<1, which may indicate that the voxel {right arrowover (r)} may belong to N regions (1<N≤m), the statistical probabilityof the voxel {right arrow over (r)} belonging to the region S_(w) isP({right arrow over (r)}∈S_(w)) and Σ_(w=1) ^(N) P({right arrow over(r)}∈ S_(w))=1, in which N may denote a total number of regions whichthe voxel {right arrow over (r)} may belong to. For example, when thereference image A and the anatomic image M in the region of V({rightarrow over (r)}) are not the same, the voxel {right arrow over (r)} maybelong to two regions (such as a region S₁ and a region S₂); thestatistical probability of the voxel {right arrow over (r)} belonging tothe region S₁ is P({right arrow over (r)}∈S₁)=0.8 and, the probabilityof the voxel {right arrow over (r)} belonging to the region S₂ isP({right arrow over (r)}∈S₂)=0.2.

For the purposes of illustration, the voxels of the anatomic imageindicative of the subject belonging may be classified into two groups(e.g., one kind is a confirmed group, and the other kind is anunconfirmed group) based on the registration accuracy. The voxels in theconfirmed group may be located inside one region. The voxel in theunconfirmed group may be located at a boundary region of one or moreregions, and thus the classification of the voxels of boundary regionsmay need to be further analyzed.

In step 207, the first attenuation map as described in step 204 may beupdated iteratively to generate a final attenuation map. The firstattenuation map may be updated based on the probability distribution ofattenuation coefficient of voxels and PET data of the subject.

In some embodiments, a second attenuation map may be generated to obtainthe final attenuation map. In some embodiments, in an initial stage thefirst attenuation map generated in step 204 may be fixed, the PET datamay be updated based on the first attenuation map to generate PET datafor a first PET image, then the contribution of the first PET image indata field may be calculated, and the first attenuation map may beupdated to generate a second attenuation map based on the contributionof the first image in data field, for example, updating the attenuationcoefficient of each of the plurality of the regions of the anatomicimage.

$\begin{matrix}{{\mu_{j}^{({n,{m + 1}})} = {\mu_{j}^{({n,m})} - \frac{\left. \frac{\partial{\overset{\sim}{L}\left( {\mu,f,p} \right)}}{\partial\mu_{j}} \middle| \mu_{j}^{({n,m})} \right.}{\left. {\sum_{k}\frac{\partial^{2}{\overset{\sim}{L}\left( {\mu,f,p} \right)}}{{\partial\mu_{j}}{\partial\mu_{k}}}} \middle| \mu_{j}^{({n,m})} \right.}}},} & \left( {{Formula}\mspace{14mu} 5} \right)\end{matrix}$wherein μ_(j) ^((n,m+1)) may denote the attenuation coefficientgenerated based on updating the attenuation coefficient μ_(j) ^((n,m))of voxel. n may denote the index number of iteration, m may denote theindex number of sub-iteration, and j may denote the index number ofvoxel. μ_(j) ^((n,m+1)) may be obtained based on μ_(j) ^((n,m)) byperforming the n-th iteration and the m-th sub-iteration on voxel j.μ_(j) ^((n,m)) may denote the attenuation coefficient before performingthe n-th iteration and the m-th sub-iteration on voxel j. The initialvalue of μ_(j) ^((n,m)) may be assigned based on prior knowledge, forexample, based on reference images stored in the database as describedelsewhere in the present disclosure. In some embodiments, theattenuation coefficients of the voxels of the unconfirmed group may beiteratively updated based on probability distribution of attenuationcoefficient and PET data, the attenuation coefficients of the voxels ofthe confirmed group may be iteratively updated based on PET data solely.In some embodiments, for some voxels, one iteration is sufficient. Forother voxels, for example, voxels in the boundary regions, multipleiterations may be performed to generate a final attenuation map. As anexample of the second iteration, PET data of a second PET image may begenerated by updating the PET data of the first PET image based on thesecond attenuation map. The contribution of the second PET image in datafield may be calculated, and a third attenuation map may be generated byupdating the second attenuation map based on the contribution of thesecond PET image in data field.

In some embodiments, the first attenuation map may be continuouslyupdated during a reconstruction of the PET data. The reconstruction ofthe PET data may be based on a time-of-flight (TOF) technique. Adetection time difference of a pair of annihilation gamma-rays may bemeasured and/or recorded by a TOF based method. The probability ofpresence of the pair annihilation point in each voxel on the line ofresponse (LOR) may be different depending on the detection timedifference of coincidence events. For example, TOF-PET scan may measurethe time difference between the detection of two 511 keV annihilationphotons.

For the purposes of illustration, taking a reconstruction of the PETdata acquired from the PET system for example, the PET data acquiredfrom the PET system may be updated based on an ordered-subsetexpectation maximization (OSEM) method. An updating PET data and/or PETimage based on the OSEM method may be expressed with a formula of:

$\begin{matrix}{{f_{j}^{({n,{m + 1}})} = {\frac{f_{j}^{({n,m})}}{\sum_{t,{i \in S_{m}}}{{\overset{\_}{a}}_{i}^{({n,m})}H_{ijt}}}{\sum_{t,{i \in S_{m}}}{H_{ijt}\frac{1/{ɛ_{i}(t)}}{{\sum_{k,t}{H_{ikt}f_{k}^{({n,m})}}} + \frac{{s_{i}(t)} + {r_{i}(t)}}{{\overset{\_}{a}}_{i}^{({n,m})}}}}}}},} & \left( {{Formula}\mspace{14mu} 6} \right)\end{matrix}$in which ā_(i) ^((n,m)) may denote the i-th element of an attenuationsinogram, the attenuation sinogram may be acquired after n iterations toa m-th sub-iteration of a subset, and an initial value of ā_(i) ^((n,m))may be determined based on an attenuation coefficient of a region; Insome embodiments, ā_(i) ^((n,m)) may equal to e^(−Σjlijuj) ^((n,m))(ā_(i) ^((n,m))=e^(−Σ) ^(j) ^(l) ^(ij) ^(u) ^(j) ^((n,m)) ), l_(ij) maydenote the system array of linear integral model that maps anattenuation map to an attenuation sinogram, μ_(j) ^((n,m)) may denotethe attenuation coefficient before performing the n-th iteration and them-th sub-iteration on voxel j; f_(j) ^((n,m+1)) may denote PET dataand/or PET image acquired in the n-th iteration to the m-thsub-iteration of subset, f_(j) ^((n,m)) may denote PET data and/or PETimage before performing the n-th iteration and m-th sub-iteration ofvoxel j; S_(m) may denote a m-th data subset in data space; H_(ijt) andH_(ikt) may respectively denote a system matrix; i may denote the indexnumber of LOR; k may denote the k-th voxel of PET data and/or PET image;j may denote the j-th voxel of PET data and/or PET image; t may denotethe index number of TOF bin; ε_(i)(t) may denote the normalizedcoefficient to a listmode data on a i-th LOR of a t-th TOF bin; s_(i)(t)may denote the number of scattering coincidence events on a i-th LOR ofa t-th TOF bin; r_(i)(t) may denote the number of random coincidenceevents on a i-th LOR of a t-th TOF bin.

The contribution of the PET data in data field may be calculated. Anexpectation of a voxel of a TOF sinogram may be determined with aformula of:y _(i) ^((n,m+1)) =ā _(i) ^((n,m))Σ_(j,t) H _(ijt) f _(j)^((n,m+1)),  (Formula 7)where y _(i) ^((n,m+1)) may denote an expectation of a voxel of a TOFsinogram, the voxel may be one voxel in a PET image, which may have beenperformed a n-th iteration and a m-th sub-iteration.

A probability function may be used to reconstruct the PET data. Forexample, the probability function may be a likelihood function, such asa penalized likelihood function, which may be shown with a formula of:

(μ,f,p)=Π_(i)({circumflex over (p)} _(i))^(pi)(p_(i)!)⁻¹exp(−{circumflex over (p)} _(i)),  (Formula 8)where{circumflex over (p)} _(i)=Σ_(j) H _(i,j) f _(j) e ^(−Σjli,juj) +s _(i)+r _(i),  (Formula 9)

In Formula 9, {circumflex over (p)}_(i) and/or p may denote originallyacquired coincidence event; i may denote the index number of LOR and/orTOF; j may denote the j-th voxel of an PET image, which may be acquiredfrom the PET data; s_(i) may denote the number of scattering coincidenceevents; r_(i) may denote the number of random coincidence events;H_(i,j) may denote system response matrix including TOF information;l_(i,j) may denote a system matrix of a line integral, which may begenerated from an attenuation map mapping to an attenuation sinogram. Alikelihood function with a parameter of probability distribution ofattenuation coefficient (such as P(μ_(j))) may be shown with a formulaof:

(μ,f,p)=Π_(i)({circumflex over (p)} _(i))^(pi)(p_(i)!)⁻¹exp(−{circumflex over (p)} _(i))Π_(j)(P(μ_(j))),  (Formula 10)

A log-likelihood function of the formula 7 may be shown with a formulaof:{tilde over (L)}(μ,f,p)=Σ_(i) p _(i) log({circumflex over (p)}_(i))−{circumflex over (p)} _(i)+

(μ_(j)),  (Formula 11)where

(μ_(j))=Σ_(j) log(P(μ_(j))).

An updated attenuation coefficient based on the OSEM mothed may bedetermined with a formula of:

$\begin{matrix}{{\mu_{j}^{({n,{m + 1}})} = {\mu_{j}^{({n,m})} + \frac{\begin{matrix}{{\sum_{i \in S_{m}}{l_{ij}\frac{{\overset{\_}{y}}_{i}^{({n,{m + 1}})}}{{\overset{\_}{y}}_{i}^{({n,{m + 1}})} + s_{i} + r_{i}}\left( {{\overset{\_}{y}}_{i}^{({n,{m + 1}})} + s_{i} + r_{i} - y_{i}} \right)}} -} \\\left. \frac{\partial{{\mathbb{P}}(\mu)}}{\partial\mu_{j}} \middle| \mu_{j}^{({n,m})} \right.\end{matrix}}{\begin{matrix}{{\sum_{i \in S_{m}}{l_{ij}\frac{\left( {\overset{\_}{y}}_{i}^{({n,{m + 1}})} \right)^{2}}{{\overset{\_}{y}}_{i}^{({n,{m + 1}})} + s_{i} + r_{i}}{\sum_{k}l_{ik}}}} +} \\\left. {\sum_{k}\frac{\partial^{2}{{\mathbb{P}}(\mu)}}{{\partial\mu_{j}}{\partial\mu_{k}}}} \middle| \mu_{j}^{({n,m})} \right.\end{matrix}}}},} & \left( {{Formula}\mspace{14mu} 12} \right)\end{matrix}$where μ_(j) ^((n,m+1)) may denote the attenuation coefficient generatedbased on updating the attenuation coefficient μ_(j) ^((n,m)). n maydenote the index number of iteration, m may denote the index number ofsub-iteration, and j may denote the index number of voxel. μ_(j)^((n,m+1)) may be obtained based on μ_(j) ^((n,m)) by performing then-th iteration and the m-th sub-iteration on voxel j. μ_(j) ^((n,m)) maydenote the attenuation coefficient before performing the n-th iterationand the m-th sub-iteration on voxel j. The initial value of μ_(j)^((n,m)) may be assigned based on prior knowledge, for example, based onreference images stored in the database as described elsewhere in thepresent disclosure. l_(ij) may denote a system matrix of a lineintegral, referring to the length of the i-th LOR through the voxel j,which may be generated from an attenuation map corresponding to anattenuation coefficient; y_(i) may denote the number of annihilationphoton pairs acquired from the i-th LOR; s_(i) may denote the number ofscattering coincidence events acquired from the i-th LOR; r_(i) maydenote the number of random coincidence events acquired from the i-thLOR; y _(j) ^((n,m+1)) may denote an expectation of i-th voxel of a PETimage in TOF sinogram, the PET image may have been performed a n-thiteration and a m-th sub-iteration. It should be noted that, in someembodiments, voxels in a same region may be assigned with a sameattenuation coefficient, the iterative updating of the attenuationcoefficients may be performed to the attenuation coefficients assignedto the plurality of regions, rather than be performed to all attenuationcoefficients of the voxels of the plurality of regions. In someembodiments, one or more iterations may be performed. In someembodiments, keeping a first PET attenuation image (also referred to asa first attenuation map of PET image) fixed, an iteration may beperformed to PET data to acquire a PET image. Keeping the PET imagefixed, an iteration may be performed to the attenuation coefficients ofthe first PET attenuation image to acquire a second PET attenuationimage. In one iteration, each subset in data space corresponding to thefirst PET attenuation image may be traversed. Then, another iterationmay be performed until a criterion is satisfied and the iteration mayterminate. For instance, the criterion may be that attenuationcoefficient acquired in the n-th iteration is the same as theattenuation coefficient acquired in the (n+1)-th iteration. If thecriterion is not satisfied, a next iteration may be performed. The nextiteration may take result of last iteration as initial.

In some embodiments, an attenuation map and a PET image may be updatedalternatively. For example, the attenuation map may be updated whilekeeping the PET image fixed to generate a second attenuation map, andthen the PET image may be updated while keeping the second attenuationmap fixed to generate a second PET image. The second attenuation map maybe updated while keeping the second PET image fixed. It should be notedthat for the voxels located in a region of the plurality of regions, oneiteration may be sufficient. For the voxels located in boundary regionsof multiple regions of the plurality of regions (e.g., the voxels of theunconfirmed group), multiple iterations may be performed to determinethe belongings of the voxels. For example, designating a voxel locatedin the boundary regions as belonging to a certain regions of theplurality of regions.

In some embodiments, the reconstruction of a PET image may be performedbased on the following operations. The PET image may be acquired byreconstructing PET data, and the PET data may be attenuation correctedby an attenuation map, the attenuation map may be generated based on thefollowing steps.

Acquiring an anatomic image indicative of a subject, the anatomic imagemay be an MR image or a CT image; fetching a reference image from adatabase, the reference image may include voxel segmentationinformation; registering the reference image to the anatomic image;Segmenting the anatomic image into a plurality of regions based on thevoxel segmentation information of the reference image; generating afirst attenuation map corresponding to the anatomic image by assigningattenuation coefficients to the plurality of regions. The voxelsbelonging to one region of the plurality of regions may be assigned witha same attenuation coefficient. Updating the PET data based on the firstattenuation map to generate PET data for a first PET image.

Acquiring probability distribution of attenuation coefficient of voxelsof the plurality of regions, and dividing the voxels of the plurality ofregions into two groups, confirmed group and unconfirmed group. Thevoxels whose probability distribution is 1 may be designated asbelonging to a certain region of the plurality of regions. Probabilitydistribution of attenuation coefficient of the voxels of the unconfirmedgroup may be less than 1, and the voxels of the unconfirmed group may bedesignated as belonging to at least two regions of the plurality ofregions. Probability distribution of attenuation coefficient may beobtained based on the following steps: calculating the registrationaccuracy of the anatomic image and the reference image, and calculatingprobability distribution of attenuation based on the registrationaccuracy. The term “probability distribution of attenuation coefficient”may refer to the probability distribution of the attenuation coefficientof a voxel of the plurality of regions.

For the voxels of the unconfirmed group, updating the first attenuationmap based on the probability distribution of attenuation coefficient ofvoxels and the updated PET data to obtain a second attenuation map.Obtaining a final attenuation map based on the second attenuation map.For the voxels of confirmed group, the first attenuation map may beupdated solely based on the PET data. In some embodiments, the firstattenuation map may be updated based on probability distribution ofattenuation coefficient of voxels and the updated PET data, and a secondattenuation map may be generated. A final attenuation map may beobtained based on the second attenuation map. In some embodiments,multiple iterations may be performed to obtain the PET image and thefinal attenuation map.

FIG. 3D illustrates a final attenuation map of transverse section of theregion between lung and abdomen according to some embodiments of thepresent disclosure, which may be similar to the actual attenuation mapof the transverse section of the region between the lungs and theabdomen of the subject as shown by FIG. 3A.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, step201, step 202, step 203, step 204, step 205, step 206 and step 207 maybe performed sequentially at an order other than that described above inFIG. 2. Step 201, step 202, step 203, step 204, step 205, step 206 andstep 207 may be performed concurrently or selectively. Step 201, step202, step 203, step 204, step 205, step 206 and step 207 may be mergedinto a single step or divided into a number of steps. In addition, oneor more other operations may be performed before/after or in performingstep 201, step 202, step 203, step 204, step 205, step 206 and step 207.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “block,” “module,” “engine,” “unit,” “component,” or“system.” Furthermore, aspects of the present disclosure may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the operator's computer, partly on the operator's computer,as a stand-alone software package, partly on the operator's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe operator's computer through any type of network, including a localarea network (LAN) or a wide area network (WAN), or the connection maybe made to an external computer (for example, through the Internet usingan Internet Service Provider) or in a cloud computing environment oroffered as a service such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution—e.g., an installation onan existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities of ingredients,properties such as molecular weight, reaction conditions, and so forth,used to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. A method implemented on a computing apparatus having at least one processor and at least one computer-readable storage device, the method comprising: acquiring listmode positron emission tomography (PET) data of a subject; acquiring an initial attenuation map of the subject for PET reconstruction; during one or more iterations, generating a PET image based on an attenuation map and the listmode PET data, the attenuation map including the initial attenuation map or an updated attenuation map from a previous iteration, determining contribution of the listmode PET data in data field based on the PET image, and updating the attenuation map to obtain an updated attenuation map based on the contribution of the listmode PET data in data field, comprising: determining a probability distribution of an attenuation coefficient of each voxel of the attenuation map; and updating the attenuation map to obtain an updated attenuation map based on the contribution of the listmode PET data in data field and the probability distribution of attenuation coefficient of the voxel; and designating the attenuation map in a last iteration as the final attenuation map.
 2. The method of claim 1, wherein the acquiring the initial attenuation map of the subject for PET reconstruction comprising: acquiring an anatomic image indicative of the subject, wherein the anatomic image comprises a plurality of voxels; fetching a reference image from a database, wherein the reference image comprises voxel segmentation information; registering the reference image to the anatomic image; segmenting the anatomic image into a plurality of regions based on the voxel segmentation information of the reference image; generating the initial attenuation map by assigning attenuation coefficients to the plurality of regions of the anatomic image.
 3. The method of claim 2, wherein the anatomic image is a magnetic resonance (MR) image or a computed tomography (CT) image.
 4. The method of claim 2, the fetching the reference image is based on one or more types of characteristic information of the subject.
 5. The method of claim 4, wherein the one or more types of characteristic information comprise height, weight, gender of the subject, or an area of the subject to imaging.
 6. The method of claim 2, the registering the reference image to the anatomic image is based on at least one of optical flow, feature points, a contour, or grey scale information.
 7. The method of claim 1, the determining the probability distribution of the attenuation coefficient of the voxel comprising: calculating a statistical probability indicative of the voxel belonging to a certain region of the plurality of regions based on the registration accuracy; and acquiring the probability distribution of the attenuation coefficient of the voxel based on the statistical probability.
 8. The method of claim 7, further comprising designating, if the statistical probability is 1, the voxel as belonging to the certain region of the plurality of regions.
 9. The method of claim 8, wherein the certain region of the plurality of regions comprises a plurality of voxels with a same probability distribution of attenuation coefficient, and the plurality of voxels of the certain region are assigned with a same attenuation coefficient.
 10. The method of claim 7, further comprising designating, if the statistical probability is less than 1, the voxel as belonging to at least two regions of the plurality of regions.
 11. The method of claim 10, wherein the voxel is designated as belonging to one region of the at least two regions as the final attenuation map is obtained.
 12. An imaging system comprising: a storage configured to store a set of instructions; at least one processor in communication with the storage, wherein when executing the instructions, the at least one processor is configured to cause the imaging system to: acquire listmode PET data of a subject; acquire an initial attenuation map of the subject for PET reconstruction; during one or more iterations, generate a PET image based on an attenuation map and the listmode PET data, the attenuation map including the initial attenuation map or an updated attenuation map from a previous iteration, determine contribution of the listmode PET data in data field based on the PET image, and update the attenuation map to obtain an updated attenuation map based on the contribution of the listmode PET data in data field, the at least one processor is configured to cause the imaging system further to: determine a probability distribution of an attenuation coefficient of each voxel of the attenuation map; and update the attenuation map to obtain an updated attenuation map based on the contribution of the listmode PET data in data field and the probability distribution of attenuation coefficient of the voxel; and designate the attenuation map in a last iteration as the final attenuation map.
 13. The system of claim 12, wherein to acquire the initial attenuation map of the subject for PET reconstruction, the at least one processor is configured to cause the imaging system further to: acquire an anatomic image indicative of the subject, wherein the anatomic image comprises a plurality of voxels; fetch a reference image from a database, wherein the reference image comprises voxel segmentation information; register the reference image to the anatomic image; segment the anatomic image into a plurality of regions based on the voxel segmentation information of the reference image; generate the initial attenuation map by assigning attenuation coefficients to the plurality of regions of the anatomic image.
 14. The system of claim 13, wherein to fetch the reference image is based on one or more types of characteristic information of the subject.
 15. The system of claim 14, wherein the one or more types of characteristic information comprise height, weight, gender of the subject, or an area of the subject to imaging.
 16. The system of claim 12, wherein to determine the probability distribution of the attenuation coefficient of the voxel, the at least one processor is configured to cause the imaging system further to: calculate a statistical probability indicative of the voxel belonging to a certain region of the plurality of regions based on the registration accuracy; and acquire the probability distribution of the attenuation coefficient of the voxel based on the statistical probability.
 17. The system of claim 16, wherein the at least one processor is configured to cause the imaging system further to designate, if the statistical probability is 1, the voxel as belonging to the certain region of the plurality of regions.
 18. The system of claim 17, wherein the certain region of the plurality of regions comprises a plurality of voxels with a same probability distribution of attenuation coefficient, and the plurality of voxels of the certain region are assigned with a same attenuation coefficient.
 19. The system of claim 16, wherein the at least one processor is configured to cause the imaging system further to designate, if the statistical probability is less than 1, the voxel as belonging to at least two regions of the plurality of regions and the voxel is designated as belonging to one region of the at least two regions as the final attenuation map is obtained. 